# Performance Prediction for Multi-hop Questions

arXiv: https://arxiv.org/abs/2308.06431 · HTML: https://mlsanity.com/paper/2308.06431/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 12 Aug 2023.

## Builds on (most cited first, 24 of 24)

- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps](https://mlsanity.com/paper/2011.01060/) (2020)
- [MuSiQue: Multihop Questions via Single-hop Question Composition](https://mlsanity.com/paper/2108.00573/) (2021)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [Constructing Datasets for Multi-hop Reading Comprehension Across Documents](https://mlsanity.com/paper/1710.06481/) (2017)
- [Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval](https://mlsanity.com/paper/2009.12756/) (2020)
- [End-to-End Open-Domain Question Answering with BERTserini](https://mlsanity.com/paper/1902.01718/) (2019)
- [Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering](https://mlsanity.com/paper/1911.10470/) (2019)
- [Simple and Effective Multi-Paragraph Reading Comprehension](https://mlsanity.com/paper/1710.10723/) (2017)
- [Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering](https://mlsanity.com/paper/1908.08167/) (2019)
- [Cognitive Graph for Multi-Hop Reading Comprehension at Scale](https://mlsanity.com/paper/1905.05460/) (2019)
- [Multi-hop Question Answering](https://mlsanity.com/paper/2204.09140/) (2022)
- [Answering Complex Open-domain Questions Through Iterative Query Generation](https://mlsanity.com/paper/1910.07000/) (2019)
- [Revealing the Importance of Semantic Retrieval for Machine Reading at Scale](https://mlsanity.com/paper/1909.08041/) (2019)
- [Multi-Hop Paragraph Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/1906.06606/) (2019)
- [Adaptive Information Seeking for Open-Domain Question Answering](https://mlsanity.com/paper/2109.06747/) (2021)
- [Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering](https://mlsanity.com/paper/1810.00494/) (2018)
- [Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering](https://mlsanity.com/paper/1909.07598/) (2019)
- [WikiPassageQA: A Benchmark Collection for Research on Non-factoid Answer Passage Retrieval](https://mlsanity.com/paper/1805.03797/) (2018)
- [HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions](https://mlsanity.com/paper/2012.15534/) (2020)
- [Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Query Performance Prediction](https://mlsanity.com/paper/2202.07376/) (2022)
- [Answering Any-hop Open-domain Questions with Iterative Document Reranking](https://mlsanity.com/paper/2009.07465/) (2020)
- [Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering](https://mlsanity.com/paper/1909.07597/) (2019)
- [Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering](https://mlsanity.com/paper/2102.03741/) (2021)

## Built on by (most cited first, 4 of 4)

- [Query Performance Prediction using Relevance Judgments Generated by Large Language Models](https://mlsanity.com/paper/2404.01012/) (2024)
- [GRADE: Generating multi-hop QA and fine-gRAined Difficulty matrix for RAG Evaluation](https://mlsanity.com/paper/2508.16994/) (2025)
- [MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation](https://mlsanity.com/paper/2504.08756/) (2025)
- [PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction](https://mlsanity.com/paper/2406.04746/) (2024)

## Abstract (arXiv)

We study the problem of Query Performance Prediction (QPP) for open-domain multi-hop Question Answering (QA), where the task is to estimate the difficulty of evaluating a multi-hop question over a corpus. Despite the extensive research on predicting the performance of ad-hoc and QA retrieval models, there has been a lack of study on the estimation of the difficulty of multi-hop questions. The problem is challenging due to the multi-step nature of the retrieval process, potential dependency of the steps and the reasoning involved. To tackle this challenge, we propose multHP, a novel pre-retrieval method for predicting the performance of open-domain multi-hop questions. Our extensive evaluation on the largest multi-hop QA dataset using several modern QA systems shows that the proposed model is a strong predictor of the performance, outperforming traditional single-hop QPP models. Additionally, we demonstrate that our approach can be effectively used to optimize the parameters of QA systems, such as the number of documents to be retrieved, resulting in improved overall retrieval performance.